English(EN) Where Does Long-Context Supervision Actually Go? Effective-Context Exposure Balancing

新的EXACT方法提升了Qwen和LLaMA模型的长上下文适应性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出了一种新颖的监督分配目标EXACT，旨在提高语言模型的长上下文适应性。该方法解决了文档掩码打包训练导致目标标记有效上下文短的问题。通过根据长尾中目标标记的频率为其分配额外的权重，EXACT在NoLiMa和RULER等基准测试中，在各种Qwen和LLaMA配置上均显示出显著的改进。当证据位于距离数千个标记之外时，收益尤为显著，而短上下文上的性能保持稳定，保留了标准的问答和推理能力。 AI

影响增强了LLM的长上下文理解能力，有望提高在需要检索文档遥远部分信息任务上的性能。

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Jinchang Zhu, Jindong Li, Chengyu Zou, Rong Fu, Chao Wang, Haowei He, Menglin Yang · 2026-09-01 04:00

长上下文监督到底去向何方？有效上下文暴露平衡

arXiv:2605.10544v2 Announce Type: replace Abstract: Long-context adaptation is often viewed as window scaling, but this misses a token-level supervision mismatch: in packed training with document masking, each target token's effective context remains short. We introduce EXACT, a …

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长上下文监督到底去向何方？有效上下文暴露平衡

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